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Tor Lattimore
Tor Lattimore
DeepMind
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Tytuł
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Bandit algorithms
T Lattimore, C Szepesvári
Cambridge University Press, 2020
17622020
Unifying PAC and regret: Uniform PAC bounds for episodic reinforcement learning
C Dann, T Lattimore, E Brunskill
Advances in Neural Information Processing Systems 30, 2017
2262017
Causal bandits: Learning good interventions via causal inference
F Lattimore, T Lattimore, MD Reid
Advances in Neural Information Processing Systems 29, 2016
188*2016
Degenerate feedback loops in recommender systems
R Jiang, S Chiappa, T Lattimore, A György, P Kohli
Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 383-390, 2019
1482019
Learning with good feature representations in bandits and in rl with a generative model
T Lattimore, C Szepesvari, G Weisz
International Conference on Machine Learning, 5662-5670, 2020
1262020
Behaviour suite for reinforcement learning
I Osband, Y Doron, M Hessel, J Aslanides, E Sezener, A Saraiva, ...
arXiv preprint arXiv:1908.03568, 2019
1252019
PAC bounds for discounted MDPs
T Lattimore, M Hutter
Algorithmic Learning Theory: 23rd International Conference, ALT 2012, Lyon …, 2012
1192012
The end of optimism? an asymptotic analysis of finite-armed linear bandits
T Lattimore, C Szepesvari
Artificial Intelligence and Statistics, 728-737, 2017
1082017
Conservative bandits
Y Wu, R Shariff, T Lattimore, C Szepesvári
International Conference on Machine Learning, 1254-1262, 2016
972016
On explore-then-commit strategies
A Garivier, T Lattimore, E Kaufmann
Advances in Neural Information Processing Systems 29, 2016
892016
A geometric perspective on optimal representations for reinforcement learning
M Bellemare, W Dabney, R Dadashi, A Ali Taiga, PS Castro, N Le Roux, ...
Advances in neural information processing systems 32, 2019
742019
Model selection in contextual stochastic bandit problems
A Pacchiano, M Phan, Y Abbasi Yadkori, A Rao, J Zimmert, T Lattimore, ...
Advances in Neural Information Processing Systems 33, 10328-10337, 2020
682020
Bounded Regret for Finite-Armed Structured Bandits
T Lattimore, R Munos
632014
Near-optimal PAC bounds for discounted MDPs
T Lattimore, M Hutter
Theoretical Computer Science 558, 125-143, 2014
602014
The sample-complexity of general reinforcement learning
T Lattimore, M Hutter, P Sunehag
International Conference on Machine Learning, 28-36, 2013
602013
Garbage in, reward out: Bootstrapping exploration in multi-armed bandits
B Kveton, C Szepesvari, S Vaswani, Z Wen, T Lattimore, M Ghavamzadeh
International Conference on Machine Learning, 3601-3610, 2019
582019
Toprank: A practical algorithm for online stochastic ranking
T Lattimore, B Kveton, S Li, C Szepesvari
Advances in Neural Information Processing Systems 31, 2018
562018
Refined lower bounds for adversarial bandits
S Gerchinovitz, T Lattimore
Advances in Neural Information Processing Systems 29, 2016
512016
Optimally confident UCB: Improved regret for finite-armed bandits
T Lattimore
arXiv preprint arXiv:1507.07880, 2015
452015
General time consistent discounting
T Lattimore, M Hutter
Theoretical Computer Science 519, 140-154, 2014
452014
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